Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Imputation-boosted collaborative filtering using machine learning classifiers
Proceedings of the 2008 ACM symposium on Applied computing
Comparing State-of-the-Art Collaborative Filtering Systems
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Scholarly paper recommendation via user's recent research interests
Proceedings of the 10th annual joint conference on Digital libraries
The efficient imputation method for neighborhood-based collaborative filtering
Proceedings of the 21st ACM international conference on Information and knowledge management
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Collaborative filtering (CF) is one of the most effective types of recommender systems. As data sparsity remains a significant challenge for CF, we consider basing predictions on imputed data, and find this often improves performance on very sparse rating data. In this paper, we propose two imputed neighborhood based collaborative filtering (INCF) algorithms: imputed nearest neighborhood CF (INN-CF) and imputed densest neighborhood CF (IDN-CF), each of which first imputes the user rating data using an imputation technique, before using a traditional Pearson correlation-based CF algorithm on the resulting imputed data of the most similar neighbors or the densest neighbors to make CF predictions for a specific user. We compared an extension of Bayesian multiple imputation (eBMI) and the mean imputation (MEI) in these INCF algorithms, with the commonly-used neighborhood based CF, Pearson correlation-based CF, as well as a densest neighborhood based CF. Our empirical results show that IDN-CF using eBMI significantly outperforms its rivals and takes less time to make its best predictions.